2020: Dawn of biz intel

2020: Dawn of biz intel

2020: Dawn of biz intel

At this time of year there are a lot of ‘BI (business intelligence) trends for next year’ pieces around — I know as I’ve been asked to write enough of them. Most of them look to the year ahead and offer little more than a series of assertions.

Worse than that, they’re boring. So when I was asked to consider the future (always something I’m wary of — given predictors’ tendency to get things spectacularly wrong) I thought why not go big, and look five years ahead and make some educated guesses based on evidence we’ve gathered and partner projects Qlik is running today?

So here are ten speculations. By 2021:

 Analytics of new data sources will have undermined some long-standing business models. Take drivers’ insurance, the widening use of telematics could mean the demise of the actuarial table-based shared risk model. Classic white-collar work roles like that of the auditor look increasingly ripe for analytic automation. This is a logical continuation of the mechanisation of intellectual work — we’ve already forgotten that ‘computer’ and ‘calculator’ were people’s job titles not long ago.

 Decision makers will be making wide use of shared, immersive analytic experiences. BI development has been focussed on small form-factor devices, but the locus will now shift to very large (think wall size) touch devices. This will enable teams of colleagues to work towards decisions through the side-by-side exploration of data in real thought time.

 BI will support a wider gestalt and a fuller range of human learning styles. The visual representation of data is dominant in 2015. However, not all people that need to use data are equally visually oriented. Humans use an individual mixture of sensory inputs to learn — often defined as three learning styles — auditory/reading, visual or kinaesthetic. By 2021, business intelligence will be making use of information delivery mediums to use all these learning styles — for auditory learners, auto generated narratives in written or spoken form.

 The data literacy gap will have narrowed. Inevitably, people will become familiar and comfortable with more forms of data visualisation over the next five years, and will learn to read and use the insights in charts more readily.  Organisations will be mandating data literacy training, as they recognise it to be a driver of competitive advantage among staff.

 Personal analytics become baseline behaviour. The behaviour we see in the quantified-self movement maybe uber-geeky now, but as more data comes on stream from services and devices, this movement will rapidly become the norm as individuals analyse ‘data of me’ for self-improvement. Not just that, but, they’ll use analytics more and more as part of family life and in their communities.

More people will (finally) be making use of predictive analysis. This has been a long time coming — today, although most organisations have a few people doing more sophisticated statistical forecasting it’s not widespread; data from industry analysts has shown for years that less than 20 per cent use predictive analytics broadly. Two drivers will be critical for this barrier to be overcome. The first is using technology to ‘nudge’ non-statisticians by automatically showing them likely trends. The second driver is simply the broad availability of tools to support predictive modelling.

There will be much easier analysis of the ‘long past’. The dramatic fall in the cost of data storage will mean that by 2021, organisations will have data in accessible, readable form (i.e., not on tape back-ups) going back further in time. This will enable the algorithmic recognition and analysis of deep patterns, analysing the ‘long’ past. This could prove useful as the analytic period stretches beyond that of economic cycles, helping organisations not repeat history.

 Intelligent Decision Automation (IDA) will take in more business decisions as machines get smarter. In 2016, IDA is only handling simple tactical decisions, but as AI is applied more widely to model and learn, IDA will touch a wider range of choices and not just those can be expressed as a decision tree. Initiatives like Google making its machine learning software (TensorFlow) open source can only lead to the acceleration of the use of AI in decision making.

 More organisations will be doing decision reviews. According to Qlik-gathered data, only 23 per cent organisations check the outcome of business decisions in 2015. Given that the oft cited main reason for investing in BI is to ‘improve decision making’, this is a problem. By 2021, organisations will be modelling more decisions. ‘Decision’ will, therefore, become a BI metadata type and therefore be analysable.

 Hybridised heuristic/algorithmic management and decision making will be emerging in some organisations. The ideal management team would be one that draws together the positive aspects of human experiential learning, as expressed through heuristic decision making, with the power of algorithmic computing. By 2021, this hybrid may just be in the form of auto-generated data stories to enlighten people and extend the range of their perspective, beyond that, who knows?

So, remember what I said at the start. These are speculations! In all likelihood at least half will be wrong — either too optimistic or not ambitious enough. Or perhaps something will come along that changes everything — an outside context event (aka a ‘black swan’).

(James Richardson is Business Analytics Strategist at Qlik )

Get a round-up of the day's top stories in your inbox

Check out all newsletters

Get a round-up of the day's top stories in your inbox